MLECO-3611: Formatting fixes for generated files.
Template files updated for generated files to adhere to
coding guidelines and clang format configuration. There
will still be unavoidable violations, but most of the
others have been fixed.
Change-Id: Ia03db40f8c62a369f2b07fe02eea65e41993a523
Signed-off-by: Kshitij Sisodia <kshitij.sisodia@arm.com>
diff --git a/tests/use_case/ad/InferenceTestAD.cc b/tests/use_case/ad/InferenceTestAD.cc
index e02e923..4991a30 100644
--- a/tests/use_case/ad/InferenceTestAD.cc
+++ b/tests/use_case/ad/InferenceTestAD.cc
@@ -1,6 +1,6 @@
/*
- * SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
- * SPDX-License-Identifier: Apache-2.0
+ * SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates
+ * <open-source-office@arm.com> SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
@@ -19,10 +19,10 @@
#include <random>
#include "AdModel.hpp"
+#include "BufAttributes.hpp"
+#include "TensorFlowLiteMicro.hpp"
#include "TestData_ad.hpp"
#include "log_macros.h"
-#include "TensorFlowLiteMicro.hpp"
-#include "BufAttributes.hpp"
#ifndef AD_FEATURE_VEC_DATA_SIZE
#define AD_IN_FEATURE_VEC_DATA_SIZE (1024)
@@ -42,10 +42,12 @@
bool RunInference(arm::app::Model& model, const int8_t vec[])
{
- TfLiteTensor *inputTensor = model.GetInputTensor(0);
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
REQUIRE(inputTensor);
- const size_t copySz = inputTensor->bytes < AD_IN_FEATURE_VEC_DATA_SIZE ? inputTensor->bytes : AD_IN_FEATURE_VEC_DATA_SIZE;
+ const size_t copySz = inputTensor->bytes < AD_IN_FEATURE_VEC_DATA_SIZE
+ ? inputTensor->bytes
+ : AD_IN_FEATURE_VEC_DATA_SIZE;
memcpy(inputTensor->data.data, vec, copySz);
@@ -54,16 +56,14 @@
bool RunInferenceRandom(arm::app::Model& model)
{
- TfLiteTensor *inputTensor = model.GetInputTensor(0);
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
REQUIRE(inputTensor);
std::random_device rndDevice;
std::mt19937 mersenneGen{rndDevice()};
std::uniform_int_distribution<short> dist{-128, 127};
- auto gen = [&dist, &mersenneGen]() {
- return dist(mersenneGen);
- };
+ auto gen = [&dist, &mersenneGen]() { return dist(mersenneGen); };
std::vector<int8_t> randomInput(inputTensor->bytes);
std::generate(std::begin(randomInput), std::end(randomInput), gen);
@@ -73,19 +73,18 @@
}
template <typename T>
-void TestInference(const T *input_goldenFV, const T *output_goldenFV, arm::app::Model& model)
+void TestInference(const T* input_goldenFV, const T* output_goldenFV, arm::app::Model& model)
{
REQUIRE(RunInference(model, static_cast<const T*>(input_goldenFV)));
- TfLiteTensor *outputTensor = model.GetOutputTensor(0);
+ TfLiteTensor* outputTensor = model.GetOutputTensor(0);
REQUIRE(outputTensor);
REQUIRE(outputTensor->bytes == OFM_0_DATA_SIZE);
auto tensorData = tflite::GetTensorData<T>(outputTensor);
REQUIRE(tensorData);
- for (size_t i = 0; i < outputTensor->bytes; i++)
- {
+ for (size_t i = 0; i < outputTensor->bytes; i++) {
REQUIRE(static_cast<int>(tensorData[i]) == static_cast<int>(((T)output_goldenFV[i])));
}
}
@@ -107,9 +106,10 @@
TEST_CASE("Running golden vector inference with TensorFlow Lite Micro and AdModel Int8", "[AD]")
{
REQUIRE(NUMBER_OF_IFM_FILES == NUMBER_OF_IFM_FILES);
- for (uint32_t i = 0 ; i < NUMBER_OF_IFM_FILES; ++i) {
- auto input_goldenFV = get_ifm_data_array(i);;
- auto output_goldenFV = get_ofm_data_array(i);
+ for (uint32_t i = 0; i < NUMBER_OF_IFM_FILES; ++i) {
+ auto input_goldenFV = GetIfmDataArray(i);
+ ;
+ auto output_goldenFV = GetOfmDataArray(i);
DYNAMIC_SECTION("Executing inference with re-init")
{
@@ -123,7 +123,6 @@
REQUIRE(model.IsInited());
TestInference<int8_t>(input_goldenFV, output_goldenFV, model);
-
}
}
}